A study for expert-informed active pulmonary nodule segmentation.

Accurate segmentation of pulmonary nodule based on computed tomography (CT) images is of great significance for the diagnosis and treatment of lung cancer. However, the current popular segmentation algorithms usually do not involve expert knowledge from radiologists, thereby carrying the risk of failing to produce generalizable and trustworthy models. In this study, we develop an expert-informed active pulmonary nodule segmentation method that iteratively optimize a deep segmentation model using an active learning scheme. The uncertainties from both intermediate segmentation results and correction inputs from radiologists are combined effectively. Interactive graph interfaces are developed to enable online corrections, significantly facilitating the integration of expert knowledge from radiologists. Evaluation results on the Luna16 dataset demonstrate that the proposed approach significantly promotes the segmentation performance of pulmonary nodules. The proposed method can effectively incorporate expert knowledge of multiple radiologists into deep segmentation algorithms, which not only promote the segmentation performance, but also enhance the validity, reliability, and generalizability of computer-aided diagnosis methods.